Author

Jimin Shin

Advisor

Zhou, Qian

Committee Member

Patil, Prakash N.

Committee Member

Wu, Tung-Lung

Date of Degree

8-1-2020

Document Type

Graduate Thesis - Open Access

Major

Statistics

Degree Name

Master of Science

College

College of Arts and Sciences

Department

Department of Mathematics and Statistics

Abstract

In this thesis, we suggest a goodness-ofit test for semi-parametric copula models. We extended the pseudo in-and-out-sample (PIOS) test proposed in [17], which is based on the PIOS test in [28]. The PIOS test is constructed by comparing the pseudo "in-sample" likelihood and pseudo "out-of-sample" likelihood. Our contribution is twoold. First, we use the approximate test statistics instead of the exact test statistics to alleviate the computational burden of calculating the test statistics. Secondly, we propose a parametric bootstrap procedure to approximate the distribution of the test statistic. Unlike the nonparametric bootstrap which resamples from the original data, the parametric procedure resamples the data from the copula model under the null hypothesis. We conduct simulation studies to investigate the performance of the approximate test statistic and parametric bootstrap. The results show that the parametric bootstrap presents higher test power with a well-controlled type I error compared to the nonparametric bootstrap.

URI

https://hdl.handle.net/11668/18450

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